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zbMATH Open
Article . 1987
Data sources: zbMATH Open
SIAM Journal on Scientific and Statistical Computing
Article . 1987 . Peer-reviewed
Data sources: Crossref
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Numerical Differentiation by High Order Interpolation

Numerical differentiation by high order interpolation
Authors: Hoffman, Peter; Reddy, K. C.;

Numerical Differentiation by High Order Interpolation

Abstract

This paper deals with high order accurate approximation for functions and their derivatives by means of Chebyshev polynomial interpolations. The approximations outlined have applications for solving partial differential equations (the Burgers' equation) by pseudo-spectral methods. The approximation operators are proved to be bounded in the uniform norm. The authors give a particular attention to the role of machine precision.

Keywords

finite difference, pseudo-spectral methods, Chebyshev polynomial interpolations, machine precision, Best approximation, Chebyshev systems, Numerical differentiation, high order interpolation, Burgers' equation, finite element, Numerical interpolation, Spectral, collocation and related methods for boundary value problems involving PDEs, numerical differentiation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Average
Top 10%
Average
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